Check out the blogs that falls under the category Vector Search. Learn more about the vector search best practices – interacting with AI LLMs and more
Category: Vector Search
Vector Search at the Edge with Couchbase Mobile
Couchbase Lite isthe first database platform with cloud-to-edge support for vector search powering AI apps in the cloud and at the edge. Learn more here.
Build Performant RAG Applications Using Couchbase Vector Search and Amazon Bedrock
Enhance generative AI with Retrieval-Augmented Generation using Couchbase Capella and Amazon Bedrock for scalable, accurate results.
A Step-by-Step Guide to Preparing Data for Retrieval-Augmented Generation (RAG)
MData gathering, chunking & embedding techniques for efficient Retrieval-Augmented Generation (RAG) with Scrapy, Python, and BAAI model.
Build Faster and Cheaper LLM Apps With Couchbase and LangChain
The LangChain-Couchbase package integrates Couchbase's vector search, semantic cache, conversational cache for generative AI workflows.
Get Started With Couchbase Vector Search In 5 Minutes
Vector search and full-text search are both methods used for searching through collections of data, but they operate in different ways and are suited to different types of data and use cases.
Accelerate Couchbase-Powered RAG AI Application With NVIDIA NIM/NeMo and LangChain
Develop an interactive GenAI application with grounded and relevant responses using Couchbase Capella-based RAG and accelerate it using NVIDIA NIM/NeMo
Announcing General Availability of the C++ SDK for Couchbase
This release adds support for native C++ language to our existing comprehensive set of SDK libraries in 11 programming languages and marks a significant milestone
Enhancing GenAI for Privacy and Performance: The Future of Personalized AI with Edge Vector Databases
This article focuses on the Centralized vs. Edge Compute paradigm, exploring why a cloud to edge database with vector capability will best address challenges on data privacy, performance, and cost-effectiveness
Develop Performant RAG Apps With Couchbase and Vectorize
The teams at Couchbase and Vectorize have been working hard to bring the power of Vectorize experiments to Couchbase Capella.
How Adaptive Applications Unlock Innovation in a New AI Age
Adaptive apps use AI to intelligently, dynamically and automatically adapt to changing circumstances and users’ preferences.
Couchbase Capella™ Wins Two Awards in the 2024 Stevie American Business Awards
Couchbase recently introduced vector search in Capella to help organizations bring to market a new class of applications that are adaptive and engage users in a hyper-personalized, contextualized way.
Querying Vectors And Things That Can Go Wrong With Them
For the best vector searches, you need to be aware of slow queries caused by inefficient indexes, inefficient queries or frequently changing data, etc.
Top Posts
- Data Modeling Explained: Conceptual, Physical, Logical
- What are Embedding Models? An Overview
- Data Analysis Methods: Qualitative vs. Quantitative Techniques
- Speed, Context, and Savings: Mastering Caching in the Capella AI...
- Application Development Life Cycle (Phases and Management Models)
- What are Vector Embeddings?
- Build a Celebrity Look-Alike App With Multimodal Vector Search an...
- Semantic Search vs. Keyword Search: What’s the Difference?
- A Breakdown of Graph RAG vs. Vector RAG
